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Pedestrian quantity estimation with trajectory patterns

: Liebig, Thomas; Xu, Zhao; May, Michael; Wrobel, Stefan

Postprint urn:nbn:de:0011-n-2212313 (1.0 MByte PDF)
MD5 Fingerprint: c56c5cd08fa73656cd9a08cd076517ed
The original publication is available at
Erstellt am: 6.12.2012

Flach, P.A.:
Machine learning and knowledge discovery in databases. European conference, ECML PKDD 2012. Pt.2 : Bristol, UK, September 24-28, 2012; proceedings
Berlin: Springer, 2012 (Lecture Notes in Computer Science 7524)
ISBN: 978-3-642-33485-6 (Print)
ISBN: 978-3-642-33486-3 (Online)
ISBN: 3-642-33485-7
ISSN: 0302-9743
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2012, Bristol>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()
pedestrian quantity estimation; trajectory; gaussian process regression; graph kernels

In street-based mobility mining, traffic volume estimation receives increasing attention as it provides important applications such as emergency support systems, quality-of-service evaluation and billboard placement. In many real world scenarios, empirical measurements are usually sparse due to some constraints. On the other hand, pedestrians generally show some movement preferences, especially in closed environments, e.g., train stations. We propose a Gaussian process regression based method for traffic volume estimation, which incorporates topological information and prior knowledge on preferred trajectories with a trajectory pattern kernel. Our approach also enables effectively finding most informative sensor placements. We evaluate our method with synthetic German train station pedestr ian data and real-world episodic movement data from the zoo of Duisburg. The empirical analysis demonstrates that incorporating trajectory patterns can largely improve the traffic prediction accuracy, especially when traffic networks are sparsely monitored.